Creating the common language of scientific research.

DataJoint enables researchers to collaboratively record, analyze and share valuable research data.

Key BenefitsGet a Demo
THE DATAJOINT PLATFORM

Meet a new OS for science.

Datajoint makes scientific research reproducible and scalable.

INFORMATION CAPTURE

A new home for all of your experiment data

Record everything in one place. From your lab metadata and specific equipment configurations to trial and experimental subject records, DataJoint is your all-in-one repository.

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DATA PROCESSING

Aggregation, synchronization & harmonization

Process and synchronize parallel data sources, extract relevant information, and evaluate each side-by-side on the same time clock, regardless of instrument or modality.

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COLLABORATION

Collaborate, analyze & publish

Merge team member workflows without conflict, analyze data streams individually or as a whole, and set the stage for publishing and reproducing results.

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TESTIMONIALS

Trusted by the best

“We are biologists, not IT professionals. A lot of scientists like us are struggling with this kind of data.”

Dr. Hui-Chen Lu, Professor

Gill Center for Neuroscience

"It’s not easy to jump on someone else’s experiment. When a student leaves the lab, it takes many meetings and calls to locate and make sense of the code and the data they left behind. With DataJoint, we are building a process where the data and code are managed jointly and are readily understood by everyone else in the lab."

Dr. Lauri Nurminen, Assistant Professsor

University of Houston

"There's more willingness to experience pain up front today for better sharing - and that willingness was not there 4 years ago. We need to be clear to the NIH that we're serious about that."

Dr. Sandeep Robert Datta, Professor

Harvard Medical School

Over 100 neuroscience labs rely on DataJoint
Frequently asked questions

Dive deeper into DataJoint

What kinds of experiments can DataJoint be used for?

DataJoint Works is a general-purpose data operations platform. However its roots lie in supporting neuroscience studies, with extensive use in experiments that combine instruments for electrophysiology, stimuli, multiphoton microscopy of neuronal signals, optogenetics, behavior, histology, and many others. A collection of reference implementations – DataJoint Elements – supports a variety of data-rich studies.

Can I use DataJoint if I already have an existing data pipeline?

Having an existing data pipeline is even better. There will be some development effort required to reorganize the existing pipeline into DataJoint tables to define proper data model and computational dependencies, but all existing processing/analysis code is fully reusable.

How much coding is required to use DataJoint?

DataJoint requires python proficiency at the same level as other scientific packages in python (such as numpy, pandas, matplotlib, etc.).

Some basic knowledge of database design and operation is preferred (e.g. primary key, foreign key, joins, normalization, etc.).

How do I begin?

Materials to learn the basics of DataJoint can be found at DataJoint Python and DataJoint Tutorials

Reference implementations of data pipelines for various neurophysiology data modalities and analyses can be found at DataJoint Elements

Where will my data reside?

The large data files (e.g. raw files) and bulky processed results are stored as files on cloud object storage or on-premise file servers, with points to these files managed seamlessly within the DataJoint framework.

The metadata and/or computational results (smaller in size) are stored directly within the database (MySQL). The database server itself can be hosted on the cloud or on-premise.

How can I take all my data out?

Access and interaction with the data can be done via programming APIs in python (DataJoint-python) or MATLAB (DataJoint-MATLAB).

Furthermore, as DataJoint is built on top of relational databases (MySQL), users can use any SQL-supported tools to access the data.

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